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Purpose <p>To develop and demonstrate a tool for efficient extraction, management, and visualization of DICOM metadata for facilitating retrospective protocol auditing and longitudinal trend analysis in diagnostic radiology.</p> Methods <p>We developed the DICOM Tags Database as an extension module of IndoQCT platform using Python, PyQt5, and SQLite. The software features a dynamic workflow allowing users to configure custom database schemas by selecting tags directly from a DICOM viewer. The system includes integrated visualization tools for generating trend lines and frequency histograms. To evaluate the tool, anonymized thoracic CT datasets from three different hospitals using diverse scanner vendors (Toshiba, Siemens, and GE) were analyzed.</p> Results <p>The software successfully extracted and archived key dosimetric and mechanical parameters, including CTDIvol, tube voltage, exposure time, slice thickness, and table height. Longitudinal visualization shows specific protocol trends, thereby identifying outliers in slice thickness settings and highlighting vendor-specific variations in coordinate system definitions.</p> Conclusions <p>The DICOM Tags Database bridges the gap between manual data recording and complex command-line utilities.</p>

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Development of an integrated DICOM tag database tool for protocol evaluation and longitudinal trend analysis

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Choirul Anam,
  • Ariij Naufal,
  • Heri Sutanto,
  • Evi Setiawati,
  • Geoff Dougherty

摘要

Purpose

To develop and demonstrate a tool for efficient extraction, management, and visualization of DICOM metadata for facilitating retrospective protocol auditing and longitudinal trend analysis in diagnostic radiology.

Methods

We developed the DICOM Tags Database as an extension module of IndoQCT platform using Python, PyQt5, and SQLite. The software features a dynamic workflow allowing users to configure custom database schemas by selecting tags directly from a DICOM viewer. The system includes integrated visualization tools for generating trend lines and frequency histograms. To evaluate the tool, anonymized thoracic CT datasets from three different hospitals using diverse scanner vendors (Toshiba, Siemens, and GE) were analyzed.

Results

The software successfully extracted and archived key dosimetric and mechanical parameters, including CTDIvol, tube voltage, exposure time, slice thickness, and table height. Longitudinal visualization shows specific protocol trends, thereby identifying outliers in slice thickness settings and highlighting vendor-specific variations in coordinate system definitions.

Conclusions

The DICOM Tags Database bridges the gap between manual data recording and complex command-line utilities.